
import pandas as pd
import matplotlib.pyplot as plt
! pip install seaborn

Collecting seaborn
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Installing collected packages: seaborn
Successfully installed seaborn-0.11.2


import seaborn as sns
heart_stroke = pd.read_csv("healthcare-dataset-stroke-.csv.csv")
heart_stroke.head(10)


heart_stroke.isna().sum()

id                     0
gender                 0
age                    0
hypertension           0
heart_disease          0
ever_married           0
work_type              0
Residence_type         0
avg_glucose_level      0
bmi                  201
smoking_status         0
stroke                 0
dtype: int64


heart_stroke.fillna(heart_stroke['bmi'].mean(), inplace=True)
heart_stroke.head(10)


heart_stroke.isna().sum()

id                   0
gender               0
age                  0
hypertension         0
heart_disease        0
ever_married         0
work_type            0
Residence_type       0
avg_glucose_level    0
bmi                  0
smoking_status       0
stroke               0
dtype: int64


s_active = heart_stroke[heart_stroke['stroke'] == 1]
s_inactive = heart_stroke[heart_stroke['stroke'] == 0]


active_age = pd.DataFrame(s_active["age"])
inactive_age = pd.DataFrame(s_inactive["age"])

active_age.head()


fig, ax = plt.subplots(figsize=(10, 6))

heart_stroke['age'] = heart_stroke['age'].astype(int)

rate = []
for i in range(heart_stroke['age'].min(), heart_stroke['age'].max()):
    rate.append(heart_stroke[heart_stroke['age'] < i]['stroke'].sum() / len(heart_stroke[heart_stroke['age'] < i]['stroke']))

sns.lineplot(data=rate ,ax=ax);

ax.set(xlabel = 'Age', ylabel='Rate of getting Heart stroke')

/srv/conda/envs/notebook/lib/python3.6/site-packages/ipykernel_launcher.py:7: RuntimeWarning: invalid value encountered in long_scalars
  import sys

[Text(0.5, 0, 'Age'), Text(0, 0.5, 'Rate of getting Heart stroke')]


# KERNEL DISTRIBUTION PLOT
# giveS the probability density

fig, ax = plt.subplots(nrows=2, 
                       ncols=1, 
                       figsize=(8, 10))


ax[0].set(title='Density plot of Age having heart stroke')
sns.kdeplot(active_age["age"], label="Active", ax=ax[0])


ax[1].set(title='Density plot of Age not having heart stroke')
sns.kdeplot(inactive_age["age"],label="Inactive", ax=ax[1], color="red");


# active_smoke is smoking column hacving heart stroke = 1
# inactive_smoke is smoking column hacving heart stroke = 0

active_smoke = pd.DataFrame(s_active["smoking_status"].value_counts())

active_smoke["Percentage"] = active_smoke["smoking_status"].apply(lambda x: x/sum(active_smoke["smoking_status"])*100)

active_smoke


inactive_smoke = pd.DataFrame(s_inactive['smoking_status'].value_counts())

inactive_smoke["Percentage"] = inactive_smoke["smoking_status"].apply(lambda x: x/sum(inactive_smoke["smoking_status"])*100)

inactive_smoke


fig, ax = plt.subplots(nrows=2,
                      ncols=1,
                      figsize=(8, 10))

ax[0].barh(active_smoke.index, active_smoke['Percentage'], height=0.7)

ax[1].barh(inactive_smoke.index, inactive_smoke['Percentage'], height=0.7, color='red');


! pip install plotly
! pip install cufflinks

import plotly as ps
import cufflinks as cf

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Successfully installed plotly-5.3.1 tenacity-8.0.1
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Building wheels for collected packages: cufflinks
  Building wheel for cufflinks (setup.py) ... done
  Created wheel for cufflinks: filename=cufflinks-0.17.3-py3-none-any.whl size=67922 sha256=b91331c4007993be769b6e5e7231483ec1fdcc28244d30b32240b43105ad49e8
  Stored in directory: /home/jovyan/.cache/pip/wheels/1c/db/ce/70cf35eb5a61b9ea3d34434072a8821dddc4f21eb5127e5415
Successfully built cufflinks
Installing collected packages: colorlover, cufflinks
Successfully installed colorlover-0.3.0 cufflinks-0.17.3


import plotly.express as px


px.pie(heart_stroke, values='stroke', names="gender")


px.pie(heart_stroke, values='stroke', names="smoking_status")


px.pie(heart_stroke, values='stroke', names="ever_married")


px.pie(heart_stroke, values='stroke', names="work_type")


from sklearn.model_selection import train_test_split

x = heart_stroke.drop('stroke', axis=1)

y = heart_stroke['stroke']


from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer


heart_stroke.head(10)


encoder = OneHotEncoder()

categorized_features = ['gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status']

tr = ColumnTransformer([('encoder', encoder, categorized_features)])

t_x = tr.fit_transform(x)

t_x

array([[0., 1., 0., ..., 1., 0., 0.],
       [1., 0., 0., ..., 0., 1., 0.],
       [0., 1., 0., ..., 0., 1., 0.],
       ...,
       [1., 0., 0., ..., 0., 1., 0.],
       [0., 1., 0., ..., 1., 0., 0.],
       [1., 0., 0., ..., 0., 0., 0.]])


x_train, x_test, y_train, y_test = train_test_split(t_x , y, test_size=0.2)


from sklearn.ensemble import RandomForestClassifier

mod = RandomForestClassifier()

mod.fit(x_train, y_train)

mod.score(x_test, y_test)

0.9549902152641878


a = y_test

pd.DataFrame(a).head(10)


b = mod.predict(x_test)

pd.DataFrame(b).head(10)


import numpy as np
arr1 = np.array(a.all())
arr2 = np.array(b.all())

if arr1==arr2:
    print('Model is Working !')

Model is Working !


heart_stroke['gender'] = heart_stroke['gender'].replace({'Male':0,'Female':1,'Other':-1}).astype(np.uint8)
heart_stroke['Residence_type'] = heart_stroke['Residence_type'].replace({'Rural':0,'Urban':1}).astype(np.uint8)
heart_stroke['work_type'] = heart_stroke['work_type'].replace({'Private':0,'Self-employed':1,'Govt_job':2,'children':-1,'Never_worked':-2}).astype(np.uint8)


X  = heart_stroke[['gender','age','hypertension','heart_disease','work_type','avg_glucose_level','bmi']]
y = heart_stroke['stroke']

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.3, random_state=42)


X_test.head(3)


! pip install imbalanced-learn

Collecting imbalanced-learn
  Downloading imbalanced_learn-0.8.1-py3-none-any.whl (189 kB)
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Installing collected packages: imbalanced-learn
Successfully installed imbalanced-learn-0.8.1


import imblearn
import sklearn
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LogisticRegression


#Importing SMOTE
from imblearn.over_sampling import SMOTE
sample = SMOTE()
X_train_1, y_train_1 =sample.fit_resample(X_train, y_train.ravel())


logreg_pipeline = Pipeline(steps = [('scale',StandardScaler()),('LR',LogisticRegression(random_state=42))])


logreg_cv = cross_val_score(logreg_pipeline,X_train_1,y_train_1,cv=10,scoring='f1')


print('Logistic Regression mean :',cross_val_score(logreg_pipeline,X_train_1,y_train_1,cv=10,scoring='f1').mean())

Logistic Regression mean : 0.8249824460155948


penalty = ['l1','l2']
C = [0.001, 0.01, 0.1, 1, 10, 100] 

log_param_grid = {'penalty': penalty, 
                  'C': C}
logreg = LogisticRegression()
grid = GridSearchCV(logreg,log_param_grid)


logreg_pipeline = Pipeline(steps = [('scale',StandardScaler()),('LR',LogisticRegression(C=0.1,penalty='l2',random_state=42))])

logreg_pipeline.fit(X_train_1,y_train_1)

#logreg.fit(X_train_1,y_train_1)

logreg_tuned_pred   = logreg_pipeline.predict(X_test)


from sklearn.metrics import plot_confusion_matrix, classification_report,accuracy_score,f1_score
print(classification_report(y_test,logreg_tuned_pred))

print('Accuracy Score: ',accuracy_score(y_test,logreg_tuned_pred))
print('F1 Score: ',f1_score(y_test,logreg_tuned_pred))

              precision    recall  f1-score   support

           0       0.97      0.76      0.85      3404
           1       0.11      0.61      0.19       173

    accuracy                           0.75      3577
   macro avg       0.54      0.68      0.52      3577
weighted avg       0.93      0.75      0.82      3577

Accuracy Score:  0.7528655297735533
F1 Score:  0.1919561243144424

	id	gender	age	hypertension	heart_disease	ever_married	work_type	Residence_type	avg_glucose_level	bmi	smoking_status	stroke
0	9046	Male	67.0	0	1	Yes	Private	Urban	228.69	36.6	formerly smoked	1
1	51676	Female	61.0	0	0	Yes	Self-employed	Rural	202.21	NaN	never smoked	1
2	31112	Male	80.0	0	1	Yes	Private	Rural	105.92	32.5	never smoked	1
3	60182	Female	49.0	0	0	Yes	Private	Urban	171.23	34.4	smokes	1
4	1665	Female	79.0	1	0	Yes	Self-employed	Rural	174.12	24.0	never smoked	1
5	56669	Male	81.0	0	0	Yes	Private	Urban	186.21	29.0	formerly smoked	1
6	53882	Male	74.0	1	1	Yes	Private	Rural	70.09	27.4	never smoked	1
7	10434	Female	69.0	0	0	No	Private	Urban	94.39	22.8	never smoked	1
8	27419	Female	59.0	0	0	Yes	Private	Rural	76.15	NaN	Unknown	1
9	60491	Female	78.0	0	0	Yes	Private	Urban	58.57	24.2	Unknown	1

	id	gender	age	hypertension	heart_disease	ever_married	work_type	Residence_type	avg_glucose_level	bmi	smoking_status	stroke
0	9046	Male	67.0	0	1	Yes	Private	Urban	228.69	36.600000	formerly smoked	1
1	51676	Female	61.0	0	0	Yes	Self-employed	Rural	202.21	28.893237	never smoked	1
2	31112	Male	80.0	0	1	Yes	Private	Rural	105.92	32.500000	never smoked	1
3	60182	Female	49.0	0	0	Yes	Private	Urban	171.23	34.400000	smokes	1
4	1665	Female	79.0	1	0	Yes	Self-employed	Rural	174.12	24.000000	never smoked	1
5	56669	Male	81.0	0	0	Yes	Private	Urban	186.21	29.000000	formerly smoked	1
6	53882	Male	74.0	1	1	Yes	Private	Rural	70.09	27.400000	never smoked	1
7	10434	Female	69.0	0	0	No	Private	Urban	94.39	22.800000	never smoked	1
8	27419	Female	59.0	0	0	Yes	Private	Rural	76.15	28.893237	Unknown	1
9	60491	Female	78.0	0	0	Yes	Private	Urban	58.57	24.200000	Unknown	1

	age
0	67.0
1	61.0
2	80.0
3	49.0
4	79.0

	smoking_status	Percentage
never smoked	90	36.144578
formerly smoked	70	28.112450
Unknown	47	18.875502
smokes	42	16.867470

	smoking_status	Percentage
never smoked	1802	37.070562
Unknown	1497	30.796132
formerly smoked	815	16.766098
smokes	747	15.367208

Cleaning data¶

Model Preparation (RandomforestClassifier)¶

model 2 logistic regression¶

spliting data¶

SMOTE Techniques for Oversampling Imbalance Data¶

	stroke
756	0
3465	0
636	0
889	0
3764	0
1795	0
4716	0
4552	0
3967	0
1680	0

	gender	age	work_type	avg_glucose_level	bmi
4688	0	31	1	64.85	23.0
4478	0	40	1	65.29	28.3
3849	1	8	255	74.42	22.5